CN110260774A - A kind of inspection of GNSS deformation information and method for early warning based on Pettitt algorithm - Google Patents
A kind of inspection of GNSS deformation information and method for early warning based on Pettitt algorithm Download PDFInfo
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- CN110260774A CN110260774A CN201910559499.2A CN201910559499A CN110260774A CN 110260774 A CN110260774 A CN 110260774A CN 201910559499 A CN201910559499 A CN 201910559499A CN 110260774 A CN110260774 A CN 110260774A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/14—Receivers specially adapted for specific applications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
Abstract
The GNSS deformation information that the invention discloses a kind of based on Pettitt algorithm is examined and method for early warning, comprising the following steps: (1) acquires the GNSS time sequence that deformable body (structures or building etc.) are carried out with deformation monitoring；(2) data in step 1 are subjected to linear fit, fitting function value is obtained by local weighted regression algorithm (LWR)；(3) data of step 2 and step 1 are made into difference, obtains the residual sequence based on local weighted regression algorithm；(4) Pettitt test statistics is constructed, the change point in trend term is obtained by the slope of fitted trend item using Pettitt algorithm using the data in step 3 as inspection data, found the changed position of deformation information in time and carry out early warning.LWR-Pettitt algorithm is used for the identification and early warning of GNSS deformation information by the present invention for the first time, is capable of the variation tendency and deformation information of accurate research and application data, has the advantages that preferable deformation information detectability and rate of false alarm are lower.
Description
Technical field
The present invention relates to the early warning field of mapping deformation monitoring, specifically a kind of GNSS deformation based on Pettitt algorithm
Information is examined and method for early warning.
Background technique
The main place of structures and building as human lives and economic development, occupies very important in the world
Status.With the rapid development of the national economy, mankind's activity is increasingly violent, associated skyscraper and large bridge become
The risks such as shape, urban surface depression and expressway slope landslide also constantly increase, and constitute to people life property safety huge
Big threat, it is necessary to deformable body effectively be monitored, deformation information is obtained, carried out at data using certain mathematical method
Reason and analysis, and then effective early-warning and predicting is carried out, reduce disaster probability of happening and coverage.It is usually used in Deformation Monitoring at present
Method be that control figure is theoretical, how accurately to carry out identification is to become to propose high-precision key.
The most commonly used is traditional Shewhart control charts and CUSUM to control nomography for existing control figure theoretical method.Control
Scheming the premise that the theoretical method of inspection carries out identification and early warning to GNSS deformation data is monitoring data palpus Normal Distribution, though
The accuracy for examining deformation data is so improved, but has to convert monitoring data before using both methods,
Have the shortcomings that computational efficiency is lower.Since the period of acquisition GNSS monitoring data is longer, it is easy the shadow by Multipath Errors
Loud and field operation environment limitation, so that a certain distribution (such as normal distribution) might not be obeyed when the data of acquisition, therefore
The scope of application of control figure theory is limited, is unfavorable for identifying GNSS deformation data the accuracy with early warning result.Therefore, it builds
A kind of pair of monitoring data are found to be not necessary to obey certain specific distribution and can efficiently identify to have with the method for early warning deformation data
Extremely important meaning.
Summary of the invention
In view of the problems of the existing technology, it is to be solved by this invention be to provide it is a kind of based on Pettitt algorithm
GNSS deformation information is examined and method for early warning.LWR-Pettitt algorithm is used for the identification of GNSS deformation information by the present invention for the first time
With early warning, it is capable of the variation tendency and deformation information of accurate research and application data, there is preferable deformation information detectability
The lower advantage with rate of false alarm.
The present invention realizes that goal of the invention adopts the following technical scheme that
The present invention provides a kind of GNSS deformation information based on Pettitt algorithm and examines and method for early warning, and steps are as follows:
Step 1, the GNSS time sequence that deformable body (structures or building etc.) are carried out with deformation monitoring is acquired；
Step 2, the data in step 1 are fitted, the method used is local weighted regression algorithm (LWR), is obtained
Functional value after fitting；
Step 3, it is poor the data of the data of step 2 and step 1 make, and obtains the residual sequence based on LWR；
Step 4, pass through fitted trend using Pettitt algorithm using the LWR residual sequence in step 3 as inspection data
The slope of item obtains the change point in trend term, finds the changed position of deformation information in time and carries out early warning.
Preferably, a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 examine with it is pre-
Alarm method, it is characterised in that in step 1, acquire the GNSS time sequence that deformation monitoring is carried out to deformable body (structures or building)
Column carry out preliminary analysis to time series, calculate mean μ and standard deviation sigma.
Preferably, a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 examine with it is pre-
Alarm method, it is characterised in that in step 2, described for approximating method LWR, LWR needs a weight function and neighborhood ginseng
Number (Size of Neighborhood) just can be carried out calculating, shown in the calculating of key parameter such as formula (1) and (2):
Wherein, Neighbourhood parameter we be arranged by using the d value of Euclidean distance, as shown in formula (1):
The setting of weight function uses cube weighting function method, then the weighting function W (di) in weighted least squares regression
It is as follows:
W(di)=(1-di 3)3,0≤d≤1 (2)
In short, the principle of this method are as follows: utilize and pass through weighted least-squares method at the every bit in independent variable space
Local fit is carried out, a polynomial function is fitted to, as the estimation of regression function at this point.
Preferably, a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 examine with it is pre-
Alarm method, it is characterised in that in step 3, the data based on local weighted regression residuals sequence of solution have 2, adopt in step 1
The monitoring data of collection, the LWR functional value being fitted in step 2, it is poor that two kinds of data are made.
Preferably, a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 examine with it is pre-
Alarm method, it is characterised in that in step 4, according to LWR residual error data, construct Pettitt test statistics, and pass through fitted trend
The slope of item obtains the change point in trend term, concrete thought are as follows:
Step 4.1: provide a time series X (t) has a change point at τ, in order to examine average value not change
Null hypothesis H0H is assumed with the changed substitution of average value1, data point is being generated, the data before and after τ carry out the ratio based on order
Compared with Pettitt statistic is expressed as k (τ), shown in calculation formula such as formula (3):
In formula:
Step 4.2: in order to determine k (s) maximum absolute value time, define two statistics respectively such as formula (4)
(5) shown in:
In formula, K refers to final Pettitt statistic, and T refers to the position of corresponding change point.Associated
Significance probability is to H0Refusal be approximately:
P≈2exp[-6K2(i3+i2)] (6)
If P < 0.5, then it is assumed that be changed significantly.
Step 4.3: for the data in step 4.2, the change point in trend term is obtained by the slope of fitted trend item,
The changed position of deformation information is found in time and carries out early warning.
Compared with prior art, the beneficial effects of the present invention are: LWR-Pettitt algorithm is used for GNSS for the first time by the present invention
The identification and early warning of deformation information are capable of the variation tendency and deformation information of accurate research and application data, have preferable become
Shape information detectability and the lower advantage of rate of false alarm.
Detailed description of the invention
Fig. 1 be a kind of GNSS deformation information based on Pettitt algorithm of the present invention examine with it is the one of method for early warning preferably real
Apply the flow chart of example；
Fig. 2 is the initial data in case of the present invention；
Fig. 3 is upper and lower offset-type data and fitting function value in case of the present invention
Fig. 4 is positive trend offset-type data and fitting function value in case of the present invention；
Fig. 5 is upper and lower offset-type data LWR residual plot of the invention；
Fig. 6 is positive trend offset-type number LWR of the invention according to residual plot；
Fig. 7 is upper and lower offset-type data Pettitt Long-term change trend figure of the invention；
Fig. 8 is positive trend offset-type data Pettitt Long-term change trend figure of the invention.
Specific embodiment
The preferred embodiments of the present invention will be described in detail with reference to the accompanying drawing, so that advantages and features of the invention energy
It is easier to be readily appreciated by one skilled in the art, so as to make a clearer definition of the protection scope of the present invention.
Referring to Fig. 1, the embodiment of the present invention includes:
A kind of inspection of GNSS deformation information and method for early warning based on Pettitt algorithm, comprising the following steps:
Step 1, the GNSS time sequence that deformable body (structures or building etc.) are carried out with deformation monitoring is acquired；
Step 2, the data in step 1 are fitted, the method used is local weighted regression algorithm (LWR), is obtained
Functional value after fitting；
Step 3, it is poor the data of the data of step 2 and step 1 make, and obtains the residual sequence based on LWR；
Step 4, pass through fitted trend using Pettitt algorithm using the LWR residual sequence in step 3 as inspection data
The slope of item obtains the change point in trend term, finds the changed position of deformation information in time and carries out early warning.
By taking the GNSS monitoring time sequence of certain section of building as an example, specific implementation step is as follows:
The first step is tested by the way of simulating two groups of GNSS deformation datas.It is the GNSS prison that we simulate in Fig. 2
Measured data, sampling time are 1500s, and the sampling interval is 1s, sample frequency 1Hz.This time experiment uses the change of two kinds of different characteristics
Shape information mode carries out.First group of deformation data is added respectively at 300-360,420-480 of the GNSS monitoring data of simulation
Enter the deformation information that value is 4cos (4 π), -4cos (4 π).Second group of deformation data is by the 1200- in analogue data
The trend term deformation data of 2x is added at 1350 to be deformed.
Second step describes approximating method LWR, and LWR needs a weight function and Neighbourhood parameter, and (neighborhood is big
It is small) it just can be carried out calculating, shown in the calculating of key parameter such as formula (1) and (2):
Wherein, Neighbourhood parameter we be arranged by using the d value of Euclidean distance, as shown in formula (1):
The setting of weight function uses cube weighting function method, then the weighting function W (di) in weighted least squares regression
It is as follows:
W(di)=(1-di 3)3,0≤d≤1 (2)
In short, the principle of this method are as follows: utilize and pass through weighted least-squares method at the every bit in independent variable space
Local fit is carried out, a polynomial function is fitted to, as the estimation of regression function at this point.
Upper and lower offset-type data and fitting function value are as shown in figure 3, actively trend type data and fitting function value are as schemed
Shown in 4.
The data based on local weighted regression residuals sequence of third step, solution have 2, the monitoring number acquired in the first step
According to the LWR functional value being fitted in second step, it is poor that two kinds of data are made.
Fig. 5 is the residual plot of upper and lower offset-type data, and the residual plot of positive trend offset-type data is shown in Fig. 6.
4th step constructs Pettitt test statistics according to LWR residual error data, and is obtained by the slope of fitted trend item
Change point in trend term out, concrete thought are as follows:
Step 4.1: provide a time series X (t) has a change point at τ, intermediate value is set as, in order to examine average value
The null hypothesis H not changed0H is assumed with the changed substitution of average value1, data point is being generated, the data before and after τ carry out base
In the comparison of order, Pettitt statistic is expressed as k (τ), shown in calculation formula such as formula (3):
In formula:
Step 4.2: in order to determine k (s) maximum absolute value time, define two statistics respectively such as formula (4)
(5) shown in:
In formula, K refers to final Pettitt statistic, and T refers to the position of corresponding change point.Associated
Significance probability is to H0Refusal be approximately:
P≈2exp[-6K2(i3+i2)] (6)
If P < 0.5, then it is assumed that be changed significantly.
Step 4.3: for the data in step 4.2, the change point in trend term is obtained by the slope of fitted trend item,
The changed position of deformation information is found in time and carries out early warning.
Detailed results are shown in Fig. 7, Fig. 8.
A kind of GNSS deformation information based on Pettitt algorithm proposed in Fig. 7~8 is examined and method for early warning, right
The recognition effect of exceptional value is preferable.From interpretation of result, can be obtained to draw a conclusion:
(1) inspection of two kinds of data is yielded good result, has the ability of deformation information identification and early warning；
(2) due to building statistic the influence of model error when, keeps it poor in the detection effect at both ends, becomes to identification
The initial position of graphic data or there are some influences, but confidence level with higher.With verifying as upper and lower offset-type data
Example, the Long-term change trend node which goes out are respectively as follows: 274,361,419 and 491, accurately have identified upper and lower
Initial position and the final position for deviating deformation data, substantially conform to the deformation information being added；
(3) in the inspection to positive trend offset-type data, the Pettitt statistic based on LWR residual error reflects conscientiously
The overall variation trend for having gone out data, the variation node detected is respectively 728,1240 and 1351, although the deformation detected
The initial position of the deformation information of the changed position of information and addition difference, but consider the influence of model error,
It, which is identified, still has confidence level with the result of early warning.
It is to provide a kind of GNSS deformation information inspection based on Pettitt algorithm and the pre- police by be solved by this invention
Method.LWR-Pettitt algorithm is used for the identification and early warning of GNSS deformation information by the present invention for the first time, being capable of accurate research and application
The variation tendency and deformation information of data have the advantages that preferable deformation information detectability and rate of false alarm are lower.
The above is merely preferred embodiments of the present invention, it is not intended to limit the scope of the present invention, therefore, for
For those skilled in the art, it is done within the spirit and principles of the present invention it is any modification, equally replace
It changes, retouch, improve, should all be included in the protection scope of the present invention.
Claims (5)
1. a kind of GNSS deformation information based on Pettitt algorithm is examined and method for early warning, which is characterized in that including following step
It is rapid:
Step 1, the GNSS time sequence that deformable body (structures or building etc.) are carried out with deformation monitoring is acquired；
Step 2, the data in step 1 are fitted, the method used is local weighted regression algorithm (LWR), is fitted
Functional value afterwards；
Step 3, it is poor the data of the data of step 2 and step 1 make, and obtains the residual sequence based on LWR；
Step 4, pass through fitted trend item using Pettitt algorithm using the LWR residual sequence in step 3 as inspection data
Slope obtains the change point in trend term, finds the changed position of deformation information in time and carries out early warning.
2. a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 is examined and method for early warning, special
Sign is in step 1, the GNSS time sequence that deformation monitoring is carried out to deformable body (structures or building) is acquired, to time sequence
Column carry out preliminary analysis, calculate mean μ and standard deviation sigma.
3. a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 is examined and method for early warning, special
Sign is in step 2, and approximating method LWR is described, and LWR needs a weight function and Neighbourhood parameter (Size of Neighborhood)
It just can be carried out calculating, shown in the calculating of key parameter such as formula (1) and (2):
Wherein, Neighbourhood parameter we be arranged by using the d value of Euclidean distance, as shown in formula (1):
The setting of weight function uses cube weighting function method, then the weighting function W (di) in weighted least squares regression is as follows:
W(di)=(1-di 3)3,0≤d≤1 (2)
In short, the principle of this method are as follows: carried out using at the every bit in independent variable space by weighted least-squares method
Local fit is fitted to a polynomial function, as the estimation of regression function at this point.
4. a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 is examined and method for early warning, special
Sign is in step 3 that the data based on local weighted regression residuals sequence of solution have 2, the monitoring number acquired in step 1
According to the LWR functional value being fitted in step 2, it is poor that two kinds of data are made.
5. a kind of GNSS deformation information based on Pettitt algorithm according to claim 1 is examined and method for early warning, special
Sign is in step 4, according to LWR residual error data, constructs Pettitt test statistics, and obtain by the slope of fitted trend item
Change point in trend term out, concrete thought are as follows:
Step 4.1: provide a time series X (t) has a change point at τ, in order to examine that average value do not change zero
Assuming that H0H is assumed with the changed substitution of average value1, data point is being generated, the data before and after τ carry out the comparison based on order,
Pettitt statistic is expressed as k (τ), shown in calculation formula such as formula (3):
In formula:
Step 4.2: in order to determine k (s) maximum absolute value time, define two statistics respectively such as formula (4) and (5)
It is shown:
In formula, K refers to final Pettitt statistic, and T refers to the position of corresponding change point.Associated is significant
Property probability is to H0Refusal be approximately:
P≈2exp[-6K2(i3+i2)] (6)
If P < 0.5, then it is assumed that be changed significantly.
Step 4.3: for the data in step 4.2, the change point in trend term being obtained by the slope of fitted trend item, in time
It was found that the changed position of deformation information and carrying out early warning.
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| CN113221070A (en) * | 2021-05-14 | 2021-08-06 | 深圳市安泰数据监测科技有限公司 | Deformation data prediction method, device and equipment for geotechnical engineering |
| CN113435763A (en) * | 2021-07-05 | 2021-09-24 | 苗懿 | Task automatic getting system and method based on big data |
| CN114299693A (en) * | 2021-12-30 | 2022-04-08 | 中国有色金属长沙勘察设计研究院有限公司 | GNSS-based slope monitoring and early warning method |
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| CN111272061B (en) * | 2020-03-16 | 2021-10-29 | 中南大学 | Nondestructive testing device and method for box-shaped transformer eddy current of tin foil paper in carton |
| CN113221070A (en) * | 2021-05-14 | 2021-08-06 | 深圳市安泰数据监测科技有限公司 | Deformation data prediction method, device and equipment for geotechnical engineering |
| CN113435763A (en) * | 2021-07-05 | 2021-09-24 | 苗懿 | Task automatic getting system and method based on big data |
| CN114299693A (en) * | 2021-12-30 | 2022-04-08 | 中国有色金属长沙勘察设计研究院有限公司 | GNSS-based slope monitoring and early warning method |
| CN116434478A (en) * | 2022-12-30 | 2023-07-14 | 深圳市地质局 | Intelligent early warning response method, device and system for geological disasters |
| CN116434478B (en) * | 2022-12-30 | 2023-11-21 | 深圳市地质局 | Intelligent early warning response method, device and system for geological disasters |
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